Nearest neighbour regression outperforms model-based prediction of specific star formation rate

Data in astronomy is rapidly growing with upcoming surveys producing 30 TB of images per night. Highly informative spectra are too expensive to measure for each detected object, hence ways of reliably estimating physical properties from images alone are paramount. The objective of this work is to test whether a “big data ready” k-nearest neighbour regression can successfully estimate the specific star formation rate (sSFR) from colours of low-redshift galaxies. The nearest neighbour algorithm achieves a root mean square error (RMSE) of 0.30, outperforming the state-of-the-art astronomical model achieving a RMSE of 0.36.